A Wind Turbine Vibration Monitoring System for Predictive Maintenance Based on Machine Learning Methods Developed under Safely Controlled Laboratory Conditions

David Pérez Granados, Mauricio Alberto Ortega Ruiz (), Joel Moreira Acosta, Sergio Arturo Gama Lara, Roberto Adrián González Domínguez and Pedro Jacinto Páramo Kañetas ()
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David Pérez Granados: Engineering Department, CIIDETEC-Coyoacán, Universidad del Valle de México, Coyoacán 04910, Mexico
Mauricio Alberto Ortega Ruiz: Engineering Department, CIIDETEC-Coyoacán, Universidad del Valle de México, Coyoacán 04910, Mexico
Joel Moreira Acosta: Engineering Department, CIIDETEC-Tuxtla, Universidad del Valle de México, Tuxtla 29056, Mexico
Sergio Arturo Gama Lara: Engineering Department, CIIDETEC-Toluca, Universidad del Valle de México, Toluca 52164, Mexico
Roberto Adrián González Domínguez: Engineering Department, CIIDETEC-Tuxtla, Universidad del Valle de México, Tuxtla 29056, Mexico
Pedro Jacinto Páramo Kañetas: Engineering Department, CIIDETEC-Coyoacán, Universidad del Valle de México, Coyoacán 04910, Mexico

Energies, 2023, vol. 16, issue 5, 1-17

Abstract: Wind energy is one of the most relevant clean energies today, so wind turbines must have good health and be reliable in operation. Current wind turbines have slender and elastic structures that can be easily damaged through vibrations and compromise their health; therefore, vibration monitoring is essential to ensure safe operation. Here, we present a method for simple wind turbine vibration monitoring in the laboratory by means of an accelerometer placed on a weathervane under different scenarios, with recording of different amplitudes of vibrations caused at a constant speed of 10 km/h. The variables, trends, and data captured during vibration monitoring were then used to implement a prediction system of synthetic failure using machine learning methods such as: Medium Trees, Cubic SVN, Logistic Regression Kernel, Optimized Neural Network, and Bagged Trees, with the last demonstrating an accuracy of up to 0.87%.

Keywords: wind turbine; wind energy; machine learning; accelerometer; vibration monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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